The collection of CWB-RS datasets must take into account the users' profile, which may be gathered using a self-reported survey, as in [1]}, as well as users' neighborhood information, users' behaviors (e.g. posts) and observations (e.g. recommendations). Mining this information, however, needs to comply with privacy and company policies.
Additional challenges are presented by the necessity to cover the various reactions that students may have under exposition to combinations of disparate social media [2]}.
Social media show a complex interplay between the individual, social, and technological levels of filtering [3]}, [4]}, with substantial effects on users' behaviors.
Therefore, one of the strongest challenges is washing out the effects of the RS adopted during the data collection, which functioning is usually unknown, enabling the use of the dataset to train a CWB-RS that could propose diverse recommendations and induce different selections.
